Pith. sign in

REVIEW 2 cited by

DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2010.00400 v3 pith:DI7VME7G submitted 2020-10-01 cs.CV cs.MM

Javier Hernandez-Ortega , Ruben Tolosana , Julian Fierrez , Aythami Morales This is my paper
classification cs.CVcs.MM
keywords detectiondeepfakefakerppgvideosdatabasesdeepfakeson-physdetect
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This work introduces a novel DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. In this work we investigate to what extent rPPG is useful for the detection of DeepFake videos. The proposed fake detector named DeepFakesON-Phys uses a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video frames, analyzing and combining both sources to better detect fake videos. This detection approach has been experimentally evaluated using the latest public databases in the field: Celeb-DF and DFDC. The results achieved, above 98% AUC (Area Under the Curve) on both databases, outperform the state of the art and prove the success of fake detectors based on physiological measurement to detect the latest DeepFake videos.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Detecting AI-Generated Video: A Vision-Language Dual-View Survey

    cs.CV 2026-07 conditional novelty 6.0 of 10

    AIGC-V detection should be treated as factual fidelity verification and organized by a four-layer vision-language dual-view taxonomy spanning cues, motion, cross-modal consistency, and world-level reasoning.

  2. Physiological Signals as a Forensic Modality for Talking-Face Deepfake Detection

    cs.LG 2026-07 reject novelty 5.0 of 10

    A classifier trained on rPPG waveforms from face videos detects talking-face deepfakes with AUC 0.806, and detection difficulty varies by generator (AUC 0.690–0.985).

Pith tools